Dynamic patterns of knowledge flows across technological domains: empirical results and link prediction

TL;DR

This paper analyzes how knowledge flows evolve across technological domains using patent citation data, identifying patterns and testing link prediction methods to forecast future knowledge spillovers.

Contribution

It introduces a novel analysis of dynamic knowledge flow patterns across domains and evaluates link prediction for forecasting technological evolution.

Findings

Knowledge flows tend to increase within previously linked domains.

Katz metric effectively predicts future knowledge spillovers.

Structural changes in technology networks are characterized over time.

Abstract

The purpose of this study is to investigate the structure and evolution of knowledge spillovers across technological domains. Specifically, dynamic patterns of knowledge flow among 29 technological domains, measured by patent citations for eight distinct periods, are identified and link prediction is tested for capability for forecasting the evolution in these cross-domain patent networks. The overall success of the predictions using the Katz metric implies that there is a tendency to generate increased knowledge flows mostly within the set of previously linked technological domains. This study contributes to innovation studies by characterizing the structural change and evolutionary behaviors in dynamic technology networks and by offering the basis for predicting the emergence of future technological knowledge flows.